ASHG 2026 Best Practices for Working with AI in Variant Interpretation in Clinical Genome Analysis - ASHG
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ASHG 2026 Best Practices for Working with AI in Variant Interpretation in Clinical Genome Analysis

$85.00

Tuesday, October 20: 1:00 PM – 4:00 PM
Pricing: $60 ASHG member, $85 nonmember. Registration and advance ticket purchase required to attend.

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SKU: ashg2026-variant-interpretation Categories: ,

Description

Instructors: Livia Loureiro, Christian Marshall, Ted Higginbotham

This workshop provides a comprehensive, hands-on setting to learn from experts how to leverage artificial intelligence-based tools for analysis and interpretation in germline genomic research. Designed for participants involved in analyzing large datasets, such as exome or whole-genome sequencing in translational research or biomarker discovery, this session will cover the technical foundations of germline variant analysis and practical integration of AI as a laboratory support tool within data analysis approaches.
The workshop focuses on how AI can guide germline variant prioritization by ranking variants based on phenotype–genotype associations and predicted functional impact, aggregating and contextualizing evidence from diverse data sources, including population databases, published scientific literature, experimental functional studies, and public curated databases, and enabling systematic periodic reanalysis as new scientific evidence becomes available. Participants will explore how AI can accelerate hypothesis generation, streamline exploratory analyses, and improve the efficiency of genomic analysis. Throughout the workshop, emphasis is placed on the critical role of expert scientific review in evaluating research and genetic evidence, assessing biological relevance, and drawing evidence-based conclusions. Participants will appreciate that AI tools represent powerful supportive resources to guide research.
  1. Welcome and Overview (10 minutes):
  2. Foundations of Variant Interpretation (25 minutes):
    1. Review of variant triage (filters, population data, allele frequency, phenotype integration)
    2. Refresher on ACMG/AMP classification logic
    3. Interactive activity: participants classify a simple variant by hand
  3. How AI Works in Practice: Inside a Tertiary Analysis Platform (30 minutes)
    1. How the platform ranks variants: algorithms, models, phenotype matching
    2. What logic looks like (evidence layers, reasoning summaries, rule-level scoring)
    3. How AI is used in variant interpretation processes
    4. Best practices for verifying AI output
    5. Strengths and limitations of automated reasoning
    6. Hands-on guided demo: participants follow a demonstration on a tertiary analysis platform
    7. Discussion: “How do we decide when to trust AI?”

[10-minute break]

  1. Case Studies: AI for Identifying Causal Variants in Challenging cases (30 minutes):
    1. Real-world implementation of software tools for efficient review of NGS data
    2. Presentation of case examples
  2. Practical Exercise: Building a Hybrid Workflow (60 minutes):
    1. Small-group activity: Participants perform a full interpretation cycle inside a tertiary analysis platform:
      1. Review the AI-prioritized list
      2. Investigate evidence layers
      3. Apply ACMG criteria
      4. Compare their conclusion with the AI-generated classification
      5. Identify steps where human input altered the final decision
      6. Output: each group drafts a proposed hybrid workflow for their lab (template provided)
  3. Wrap-Up and Key Takeaways (15 minutes):
      1. Best practices for AI adoption in accredited labs
      2. How to scale hybrid workflows across teams
      3. Quality and validation considerations

Learning Objectives:

  1. Describe how AI-enabled tools support genomic variant prioritization and interpretation
  2. Assess how laboratories use AI for genomic interpretation and define validation needs for routine clinical implementation
  3. Indicate how AI reduces variant complexity
  4. Evaluate how AI-supported tools narrow thousands of variants down to a small, reportable set, and discuss the strengths and limitations
  5. Integrate best-practice genomic interpretation frameworks to ensure oversight of AI-supported results
  6. Evaluate and implement a hybrid human interpretation workflow to improve efficiency and consistency for panel and genome-scale sequencing data

Additional Information:

Intermediate level; Registered participants will be contacted by email and asked to complete a brief registration on the tertiary analysis platform in advance of the workshop. This process is expected to take under 10 minutes.

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